Covariance approximation for fast and accurate computation of channelized Hotelling observer statistics

Author

Bonetto, Paola ; Qi, Jinyi ; Leahy, Richard M.

Volume

3

fYear

1999

fDate

1999

Firstpage

1383

Abstract

We describe a method for computing linear observer statistics for maximum a posteriori (MAP) reconstructions of PET images. The method is based on a theoretical approximation for the mean and covariance of MAP reconstructions. In particular, we derive here a closed form for the channelized Hotelling observer (CHO) statistic applied to 2D MAP images. We show reasonably good correspondence between these theoretical results and Monte Carlo studies. The accuracy and low computational cost of the approximation allow us to analyze the observer performance over a wide range of operating conditions and parameter settings for the MAP reconstruction algorithm

Keywords

covariance matrices; image reconstruction; maximum likelihood estimation; medical image processing; observers; positron emission tomography; 2D MAP images; Monte Carlo studies; Poisson model; ROC curve; binary lesion detection task; channelized Hotelling observer statistics; closed form solution; computer observers; covariance approximation; fast accurate computation; linear observer statistics; low computational cost; maximum a posteriori reconstructions; nonlinear image reconstruction; positron emission tomography images; Covariance matrix; Humans; Image quality; Image reconstruction; Lesions; Positron emission tomography; Reconstruction algorithms; Statistical distributions; Statistics; Visual system;

fLanguage

English

Publisher

ieee

Conference_Titel

Nuclear Science Symposium, 1999. Conference Record. 1999 IEEE

Conference_Location

Seattle, WA

ISSN

1082-3654

Print_ISBN

0-7803-5696-9

Type

conf

DOI

10.1109/NSSMIC.1999.842813

Filename

842813